Elevation-Aware Domain Adaptation for Sematic Segmentation of Aerial Images
Recent advancements in Earth observation technologies have accelerated remote sensing (RS) data acquisition, yet cross-domain semantic segmentation remains challenged by domain shifts. Traditional unsupervised domain adaptation (UDA) methods often rely on computationally intensive and unstable gener...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-07-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/14/2529 |
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| author | Zihao Sun Peng Guo Zehui Li Xiuwan Chen Xinbo Liu |
| author_facet | Zihao Sun Peng Guo Zehui Li Xiuwan Chen Xinbo Liu |
| author_sort | Zihao Sun |
| collection | DOAJ |
| description | Recent advancements in Earth observation technologies have accelerated remote sensing (RS) data acquisition, yet cross-domain semantic segmentation remains challenged by domain shifts. Traditional unsupervised domain adaptation (UDA) methods often rely on computationally intensive and unstable generative adversarial networks (GANs). This study introduces elevation-aware domain adaptation (EADA), a multi-task framework that integrates elevation estimation (via digital surface models) with semantic segmentation to address distribution discrepancies. EADA employs a shared encoder and task-specific decoders, enhanced by a spatial attention-based feature fusion module. Experiments on Potsdam and Vaihingen datasets under cross-domain settings (e.g., Potsdam IRRG → Vaihingen IRRG) show that EADA achieves state-of-the-art performance, with a mean IoU of 54.62% and an F1-score of 65.47%, outperforming single-stage baselines. Elevation awareness significantly improves the segmentation of height-sensitive classes, such as buildings, while maintaining computational efficiency. Compared to multi-stage approaches, EADA’s end-to-end design reduces training complexity without sacrificing accuracy. These results demonstrate that incorporating elevation data effectively mitigates domain shifts in RS imagery. However, lower accuracy for elevation-insensitive classes suggests the need for further refinement to enhance overall generalizability. |
| format | Article |
| id | doaj-art-4edc190922c046a6ac5c070eb976c91e |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-4edc190922c046a6ac5c070eb976c91e2025-08-20T03:32:33ZengMDPI AGRemote Sensing2072-42922025-07-011714252910.3390/rs17142529Elevation-Aware Domain Adaptation for Sematic Segmentation of Aerial ImagesZihao Sun0Peng Guo1Zehui Li2Xiuwan Chen3Xinbo Liu4Institute of Remote Sensing and Geographic Information System, Peking University, Beijing 100871, ChinaLand Satellite Remote Sensing Application Center, Ministry of Natural Resources of P.R. China, Beijing 100048, ChinaInstitute of Remote Sensing and Geographic Information System, Peking University, Beijing 100871, ChinaInstitute of Remote Sensing and Geographic Information System, Peking University, Beijing 100871, ChinaChina Yangtze Power Co., Ltd., Yichang 443000, ChinaRecent advancements in Earth observation technologies have accelerated remote sensing (RS) data acquisition, yet cross-domain semantic segmentation remains challenged by domain shifts. Traditional unsupervised domain adaptation (UDA) methods often rely on computationally intensive and unstable generative adversarial networks (GANs). This study introduces elevation-aware domain adaptation (EADA), a multi-task framework that integrates elevation estimation (via digital surface models) with semantic segmentation to address distribution discrepancies. EADA employs a shared encoder and task-specific decoders, enhanced by a spatial attention-based feature fusion module. Experiments on Potsdam and Vaihingen datasets under cross-domain settings (e.g., Potsdam IRRG → Vaihingen IRRG) show that EADA achieves state-of-the-art performance, with a mean IoU of 54.62% and an F1-score of 65.47%, outperforming single-stage baselines. Elevation awareness significantly improves the segmentation of height-sensitive classes, such as buildings, while maintaining computational efficiency. Compared to multi-stage approaches, EADA’s end-to-end design reduces training complexity without sacrificing accuracy. These results demonstrate that incorporating elevation data effectively mitigates domain shifts in RS imagery. However, lower accuracy for elevation-insensitive classes suggests the need for further refinement to enhance overall generalizability.https://www.mdpi.com/2072-4292/17/14/2529unsupervised domain adaptationsemantic segmentationremote sensing imageself-supervisionmulti-task learning |
| spellingShingle | Zihao Sun Peng Guo Zehui Li Xiuwan Chen Xinbo Liu Elevation-Aware Domain Adaptation for Sematic Segmentation of Aerial Images Remote Sensing unsupervised domain adaptation semantic segmentation remote sensing image self-supervision multi-task learning |
| title | Elevation-Aware Domain Adaptation for Sematic Segmentation of Aerial Images |
| title_full | Elevation-Aware Domain Adaptation for Sematic Segmentation of Aerial Images |
| title_fullStr | Elevation-Aware Domain Adaptation for Sematic Segmentation of Aerial Images |
| title_full_unstemmed | Elevation-Aware Domain Adaptation for Sematic Segmentation of Aerial Images |
| title_short | Elevation-Aware Domain Adaptation for Sematic Segmentation of Aerial Images |
| title_sort | elevation aware domain adaptation for sematic segmentation of aerial images |
| topic | unsupervised domain adaptation semantic segmentation remote sensing image self-supervision multi-task learning |
| url | https://www.mdpi.com/2072-4292/17/14/2529 |
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